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| Home > Publications database > Large-scale multi-omics enhance risk prediction for type 2 diabetes. |
| Journal Article | DKFZ-2026-01298 |
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2026
BioMed Central
London
Abstract: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements.We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI).During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001).Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.
Keyword(s): Humans (MeSH) ; Diabetes Mellitus, Type 2: diagnosis (MeSH) ; Diabetes Mellitus, Type 2: epidemiology (MeSH) ; Diabetes Mellitus, Type 2: blood (MeSH) ; Diabetes Mellitus, Type 2: genetics (MeSH) ; Multiomics (MeSH) ; Metabolomics (MeSH) ; Risk Assessment (MeSH) ; Proteomics (MeSH) ; Risk Factors (MeSH) ; Biomarkers: blood (MeSH) ; Predictive Value of Tests (MeSH) ; UK Biobank (MeSH) ; Female (MeSH) ; Genetic Risk Score (MeSH) ; Prognosis (MeSH) ; Middle Aged (MeSH) ; Genetic Predisposition to Disease (MeSH) ; Male (MeSH) ; Time Factors (MeSH) ; Aged (MeSH) ; Incidence (MeSH) ; Reproducibility of Results (MeSH) ; United Kingdom: epidemiology (MeSH) ; Phenotype (MeSH) ; Glycated Hemoglobin: metabolism (MeSH) ; Decision Support Techniques (MeSH) ; Biomarker ; Cardiometabolic disease ; Multi-omics ; Prediction model ; Type 2 diabetes ; Biomarkers ; hemoglobin A1c protein, human ; Glycated Hemoglobin
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